Portable, non-destructive colorimetry and visible reflectance spectroscopy paired with machine learning can classify experimentally heat-treated silcrete from three South African sources.

Murray JK, Oestmo S, Zipkin AM.

Open source

DOI: 10.1371/journal.pone.0266389
Published: 2022-04-08
Container: PLoS One
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0266389,
  title = {Portable, non-destructive colorimetry and visible reflectance spectroscopy paired with machine learning can classify experimentally heat-treated silcrete from three South African sources.},
  author = {Murray JK and  Oestmo S and  Zipkin AM.},
  year = {2022},
  journal = {PLoS One},
  doi = {10.1371/journal.pone.0266389},
  url = {https://doi.org/10.1371/journal.pone.0266389}
}

RIS

TY  - JOUR
TI  - Portable, non-destructive colorimetry and visible reflectance spectroscopy paired with machine learning can classify experimentally heat-treated silcrete from three South African sources.
AU  - Murray JK
AU  -  Oestmo S
AU  -  Zipkin AM.
PY  - 2022
JO  - PLoS One
DO  - 10.1371/journal.pone.0266389
UR  - https://doi.org/10.1371/journal.pone.0266389
ER  -

APA

JK, M., S, O., & AM., Z. (2022). Portable, non-destructive colorimetry and visible reflectance spectroscopy paired with machine learning can classify experimentally heat-treated silcrete from three South African sources.. PLoS One. https://doi.org/10.1371/journal.pone.0266389

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europe-pmc · retrieved 2026-09-25T21:53:29.301Z
doaj · retrieved 2026-09-25T21:53:29.308Z